Visual classification can be divided into coarse-grained and fine-grained classification. Coarse-grained classification represents the classification of categories with a large degree of dissimilarity, such as the classification of cats and dogs, while fine-grained classification represents classifications with a large degree of similarity, such as bird species[1]}[2]}, dog species[3]}, and the makes or models of vehicles[4]}. "Fine-grained" refers to more fine-grained divisions under common species classification. The challenges of fine-grained visual classification task is threefold. First, there is a lot of variation in the same category. Taking birds as an example, photos of the same bird from different angles can vary greatly in color and shape, as shown in Fig.REF . The second point is that objects of different subcategories may be very similar. As shown in Fig.REF , the textures of the three birds are very similar. The third point is that unlike coarse grained classification, fine-grained classification often requires professional experts to label data, which makes data more expensive. For the above reasons, frameworks that perform very well on coarse-grained classification tasks such as ResNet[5]}, EfficientNet[6]}, Vision Transformer (ViT) [7]} will be very limited for fine-grained classification.
